{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "import spacy\n",
    "nlp = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load patent and classification information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def readTSV(fn):\n",
    "    return (pd.DataFrame(pd.read_csv(fn, sep='\\t')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "asg_df = readTSV('company_patent_info.tsv')\n",
    "asg_df = asg_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pid          int64\n",
       "abstract    object\n",
       "date        object\n",
       "name        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asg_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dictionary for nouns and verbs\n",
    "words = set()\n",
    "for idx, row in asg_df.iterrows():\n",
    "    abstr = row['abstract']\n",
    "    text_ = nlp(abstr)\n",
    "    doc_words = set(token.lemma_ for token in text_ if (token.pos_ == 'NOUN') or (token.pos_ == 'VERB'))\n",
    "    words = words.union(doc_words)\n",
    "    #print (idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#combine abstracts for the same company\n",
    "combined_asg_abstr_df = pd.DataFrame(asg_df.groupby(['name'])['abstract'].apply(lambda x: ', '.join(x)).drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Apple                              The disclosed embodiments provide a system tha...\n",
       "Bayerische motoren werke           In order to enable the temperature in the regi...\n",
       "Ericsson                           A method for controlling temperature-related t...\n",
       "Ford motor                         A tonneau system for a vehicle bed includes a ...\n",
       "Google                             A distributed storage system that includes mem...\n",
       "Huawei                             A radio frequency receiver and an automatic ga...\n",
       "Intel Corporation                  An antenna assembly comprises a computer expan...\n",
       "International business machines    A computer-implemented method and system are p...\n",
       "Nokia Corporation                  The specification and drawings present a new m...\n",
       "PepsiCo, Inc.                      A beverage can with top and bottom heat barrie...\n",
       "Qualcomm                           Multi-ported memory systems (e.g., register fi...\n",
       "Shell Oil Company                  Contact of a crude feed with one or more catal...\n",
       "The Coca-Cola Company              A blending plan for concentrated consumable pr...\n",
       "Toyota                             A three phase stator configured with phases th...\n",
       "Name: abstract, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_asg_abstr_df.reset_index()\n",
    "#combined_asg_abstr_df.columns = ['name', 'abstract']\n",
    "combined_asg_abstr_df['abstract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(vocabulary=words, ngram_range=(1,1))\n",
    "tfidf_vectors = tfidf_vectorizer.fit_transform(combined_asg_abstr_df['abstract'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 31267)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vectors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use t-SNE\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "tsne = TSNE(n_components=2, random_state=0, n_iter=10000, perplexity=5)\n",
    "np.set_printoptions(suppress=True)\n",
    "result = tsne.fit_transform(tfidf_vectors.toarray())\n",
    "labels = asg_list\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(result[:, 0], result[:, 1], c='orange', edgecolors='r')\n",
    "for label, x, y in zip(labels, result[:, 0], result[:, 1]):\n",
    "    plt.annotate(label, xy=(x+1, y+1), xytext=(0, 0), textcoords='offset points')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "row_list = []\n",
    "for i in range(tfidf_vectors.shape[0]):\n",
    "    for j in range(i+1, tfidf_vectors.shape[0]):\n",
    "        w = linear_kernel(tfidf_vectors[i], tfidf_vectors[j]).flatten()\n",
    "        row_list.append({\"weight\": w, \"asg_A\": asg_list[i], \"asg_B\": asg_list[j]})\n",
    "company_cosine_similarity_tfidf_df = pd.DataFrame(row_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1aaf86f7d0>]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(company_cosine_similarity_tfidf_df['weight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously, cosine similarity is not suitable for comparing companies using their tfidf matrix."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py3-tf2]",
   "language": "python",
   "name": "conda-env-py3-tf2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
